brahmaputra river flood case study

• 0 Shopping Cart

Case Study – Ganges/Brahmaputra River Basin

Flooding is a significant problem in the Ganges and Brahmaputra river basin. They cause large scale problems in the low lying country of Bangladesh. There are both human and natural causes of flooding in this area.

Human Causes

Deforestation – Population increase in Nepal means there is a greater demand for food, fuel and building materials. As a result, deforestation has increased significantly. This reduces interception and increases run-off. This leads to soil erosion . River channels fill with soil, the capacity of the River Ganges and Brahmaputra is reduced and flooding occurs.

Natural Causes

• Monsoon Rain
• Melting Snow
• Tectonic Activity – The Indian Plate is moving towards the Eurasian Plate. The land where they meet (Himalayas) is getting higher and steeper every year ( fold mountains ). As a result, the soil becomes loose and is susceptible to erosion. This causes more soil and silt in rivers. This leads to flooding in Bangladesh.

Premium Resources

Please support internet geography.

If you've found the resources on this page useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Related Topics

Use the images below to explore related GeoTopics.

Kerala Floods 2018

Topic home, the great floods of 2000, share this:.

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to share on Pinterest (Opens in new window)
• Click to email a link to a friend (Opens in new window)
• Click to share on WhatsApp (Opens in new window)
• Click to print (Opens in new window)

If you've found the resources on this site useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Search Internet Geography

Top posts and pages.

Latest Blog Entries

Pin It on Pinterest

• Click to share
• Print Friendly

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia

Roles Conceptualization, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing

Affiliation Department of Geoinformatics, Central University of Jharkhand, Ranchi, Jharkhand, India

Roles Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Writing – original draft

* E-mail: [email protected]

Affiliation Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI, United States of America

• Amit Kumar, 
• Subhasree Mondal, 

• Published: August 22, 2022
• https://doi.org/10.1371/journal.pone.0273384
• Reader Comments

The present study is focused on the flood inundation in Brahmaputra Basin, which is one of the most recurrent and destructive natural disasters of the region. The flood inundation was assessed using C-Band Sentinel 1A synthetic aperture radar (SAR) during 2015–2020 with precipitation patterns, runoff discharge, and their impacts on land cover in the basin. The study exhibited a very high precipitation during monsoon in the upper catchment resulting in severe flood inundation in downslopes of Brahmaputra Basin. A very high (900–2000 mm) to extremely high (>2000 mm) monthly cumulative precipitation in the south and south-eastern parts of basin led to high discharge (16,000 to 18,000 m 3 s -1 ) during July-August months. The river discharge increases with cumulative effects of precipitation and melting of snow cover during late summer and monsoon season, and induced flood inundation in lower parts of basin. This flood has largely affected agricultural land (>77% of total basin), forests (~3%), and settlement (426 to 1758 km 2 ) affecting large wildlife and livelihood during 2015–2020. The study highlights the regions affected with recurrent flood and necessitates adopting an integrated, multi-hazard, multi-stakeholder approach with an emphasis on self-reliance of the community for sustenance with local resources and practices.

Citation: Kumar A, Mondal S, Lal P (2022) Analysing frequent extreme flood incidences in Brahmaputra basin, South Asia. PLoS ONE 17(8): e0273384. https://doi.org/10.1371/journal.pone.0273384

Editor: Bijeesh Kozhikkodan Veettil, Đại Học Duy Tân: Dai Hoc Duy Tan, VIET NAM

Received: April 1, 2022; Accepted: August 7, 2022; Published: August 22, 2022

Copyright: © 2022 Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is available in public repository https://doi.org/10.6084/m9.figshare.20152751.v1 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The trend of global warming induces longer conditions of no rain followed by a sudden bout of excessive precipitation, causing extreme weather events particularly floods [ 1 – 3 ]. The increasing temperature produces more energy in the Earth’s system, specifically increasing the probability of evaporation from the surface water and oceans augmenting cloud formation [ 4 ]. At higher temperatures, the air can hold more moisture content that tends to rise in the precipitation intensity, its duration, and frequency [ 5 – 7 ]. Therefore, floods are triggered by intense precipitation, anomalous longer duration, close repetition of precipitations, topographical characteristics or a combination of all these phenomenon [ 8 – 11 ]. For example, an extended period of very heavy precipitation leads to the development of a low-pressure system apart from extended monsoon depression during the Kerala flood in 2019 [ 12 – 14 ].

In the twenty-first century, floods emerged as one of the most frequent and dangerous natural hazards worldwide [ 15 – 18 ]. from the flood mostly occurs due to torrential precipitation, but there are various factors such as poor drainage system and water storage management, rapid land transformation due to anthropogenic influences, river structure and relief of region, which are also major contributing factors augmenting the severity of extreme flood incidents [ 19 – 21 ]. Particularly in India sub-continent flood is a very common natural disaster during the southwest monsoon (June-August) [ 22 ]. The combined attribution of monsoonal precipitation and rapid melting of Himalayan glaciers accelerates the downwards flows of water in river during the summer season [ 23 , 24 ] which makes a very high flow rate of water compared to the capacity of the river [ 25 – 27 ]. Most of the Indian river originating from Himalayan region has similar nature of flow, during late summer and monsoon river flow increases by multiple times with combined melting of snow and heavy precipitation. Among all the different rivers originating from Himalayan regions, Brahmaputra river in eastern India is one of the most affected flood hazard regions in the world [ 28 ]. The Brahmaputra basin is affected every year by catastrophic floods. The melting of eastern Himalayan glaciers during and before the Indian summer monsoon intensifies downstream and causes flood in valley of Northeast India [ 29 ]. The factors for flood severity apart from high-flow of river includes rampant deforestation [ 30 ], high impervious surface expansion are related to anthropogenic influences rising flood severity. Around 4,500 km embankment along the Brahmaputra is enclosed by the rivers that lead to an acceleration in the river flow and often increase the flood susceptibility [ 31 ]. The 65% of annual precipitation (being of the order of 165 cm) in the lower basin (Assam) is caused by the South-West monsoon. The major parts of the runoff in the river were introduced through the heavy precipitation (~ 510–640 cm) in Arunachal Pradesh (Abo and Mishmi hill), and moderately high precipitation (250–510 cm) in the Brahmaputra plain. Therefore, in the present study, the spatial patterns of frequent flood inundation in the Brahmaputra basin were analysed for the recent years (2015–2020) and its impacts on the land use/land cover (LULC) were discussed.

2. Study area

Brahmaputra basin is located in the north-eastern parts of the Indian Subcontinent and lies between 23.9°N to 31.5°N latitude and 82.1°E to 97.7°E longitude ( Fig 1 ). The river Brahmaputra originates from the Angsi glacier located in the south of Tibet (at an elevation of 5300 m) and falls in the Bay of Bengal after travelling 2,880 km through Tibet, India, and Bangladesh. The one-third portion of the Indian Brahmaputra basin is in Assam state of India. The Brahmaputra Valley within Assam consists of vast alluvial floodplains covering an area of about 56480 sq. km (altitude 34130 m). The upper part of the basin lies in the trans-Himalaya (Tibet), greater Himalaya (Nepal and Arunachal Pradesh), middle Himalaya (Arunachal Pradesh), and Shivalik range (lower part of Arunachal Pradesh) as the lower part of Himalaya. The lower and middle part of the basin lies in the low-lying plain valley primarily in Assam (India) and Bangladesh, which formed due to continuous fluvial deposition of Himalayan streams and rivers. There are various subsidiary rivers, which confluence with Brahmaputra River in the low-lying Assam valley including Subansiri (originates in Tibetan region), Kameng (Indo-Tibet border), Beki and Sankosh (northern Bhutan), Manas (southern Bhutan), while rivers Teesta, Rangeet, Rangpo (originates from Sikkim) confluence in Bangladesh. The Brahmaputra and its tributaries in India (Assam), Bangladesh, and Bhutan elucidates intense environmental risk due to climate change and the extensive increase in anthropogenic activities, led to exposure to floods, riverbank erosion, landslide [ 27 ].

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0273384.g001

3. Data used and methodology

To deduce the flood inundation and its impact on the Brahmaputra basin, various concurrent SAR imagery for 2015–2020, and ESA-CCI land use/ land cover (300m) were used. The GPM precipitation of 0.1° was used for analysing monthly and the sum of seasonal precipitation (June-September) and anomalous precipitation between 2015–2020, whereas long-term mean (2001–2020) precipitation was used to estimate precipitation anomaly, with reference to the mean of the respective time period from 2000 to 2020. The Global Flood Awareness System (GloFAS) ERA5 river discharge reanalysis products having horizontal grid resolution of 0.1° were used to estimate the river discharge of Brahmaputra in the eastern-lower part of Arunachal and upper part of Bangladesh.

Due to the large basin coverage, the Google Earth Engine (GEE) platform was used to execute the task impeccably for inundation mapping and to deduce flood impact on LULC on a parallel processing architecture with a powerful web platform for cloud-based processing for remote sensing datasets. The Sentinel 1A/1B GRD datasets of the VH polarisation with ascending pass were retrieved from ESA for the concurrent flood during 2015–2020 in the Brahmaputra basin. The orbit correction and noise removal methods were applied to improve the geometric accuracy by ~10 cm [ 32 ] and remove border noise and low-intensity noise. Furthermore, radiometric calibration followed by a Lee sigma Speckle filter (5x5 window size) was performed for the qualitative assessment using the backscatter coefficient and making the image more interpretable. The Terrain correction applied to the filtered image employing the Shuttle Radar Topographic Mission (SRTM) one arc-second digital elevation model (DEM) to rectify the distortions including foreshortening, layover, or shadowing effects. The GRD data of Sentinel 1A/1B provided by ESA has geometric distortions due to complex terrain and when the sensor is not pointing directly at the Nadir location so ortho-rectification was necessary before the classification of flood and non-flood data. The ortho-rectification is a substitute of geo-referencing method of remote sensing datasets. It converts images into suitable form for maps by removing the sensor, satellite motion and surface related geometric distortions from Level 1 SAR imagery. Later, pre-processed image backscatter coefficients were converted to decibel. For the Brahmaputra basin, the backscatter intensity of VH varies between -15 to -20 dB during the pre-flood period, while the backscatter intensity was approximately -25 to -30 dB during the post-flood analysis. Backscatter ratio methods were adopted following Lal et al. (2020) [ 17 ] and it’s one of the widely used techniques for flood inundation mapping [ 33 , 34 ]. The backscatter ratio method estimates the changes in due to flood inundation the during flood mosaic backscatter SAR data were divided from pre-flood mosaic SAR backscatter data to deduce the flood inundation. The resultant from backscatter ratio method shows a change between pre-flood and post-flood backscatter coefficient and value of 1.26 selected based on a trial-and-error approach. Using the resultant value binary image was created for flood inundation, value above 1.26 assigned as 1 (flood-inundated pixel) and value below 1.26 assigned as 0 (no-data). The flood inundation binary images show all the permanent water bodies as flood pixels, the JRC Global Surface Water dataset of 30 m resolution was used to refine the flood water classification by masking the surface water bodies. A digital elevation model (WWF Hydro SHEDS, a spatial resolution of 3 arc-seconds) was used to remove blind spots having higher slopes to eliminate its confusion with flooded pixels. Furthermore, the connectivity of the flood pixels is assessed to reduce the noise. ESA-CCI land cover of 300m spatial resolution was used to glean the impact of flood inundation on different LULC classifications during respective years using overlay analysis in the GEE environment.

The code for assessing the flood inundation of any region using Sentinel 1A/1B is available at https://code.earthengine.google.com/ee2cdaeff8adab3f59c8e4a21e6868c1 .

4. Results and discussion

4.1 land use/ land cover change in brahmaputra basin.

The land use/ land cover change (LULCC) was analysed using the ESA-CCI LULC data set for the Brahmaputra basin covering part of India, Tibet, Bhutan, and Bangladesh during 2000–2019. The study exhibited the dominance of grassland (33.72%; 225905.7 km 2 ) primarily in the upper parts of the Brahmaputra basin (Tibet) followed by forest cover (30.19%; 202289.9 km 2 ) primarily in the lower Himalayan regions, and agriculture (26.89%; 180160.3 km 2 ) primarily in Assam and Bangladesh. The LULC change in the Indian and Bangladesh regions are primarily due to exponential population growth, lack of proper valuation of ecological services, biological limitation, concurrent flood disaster, and improper management of land by the public. The significant decrease was observed primarily in the agriculture (1.58%; 2853.18 km 2 ), followed by shrubland (28.66%; 3218.67 km 2 ), waterbody (3.76%; 643.41 km 2 ), bare land (0.8%; 119.25 km 2 ) and sparse vegetation (4.85%; 56.7 km 2 ) during 1992–2020. In contrast, increase was evident in settlement (290.16%; 1282.23 km 2 ) followed by wetland (4.03%; 25.11 km 2 ), forest cover (2.1%; 4240.44 km 2 ) and grassland (0.59%; 1343.43 km 2 ) ( Table 1A ). The rise in forest cover and grassland modulates the regional climate as increase in precipitation and decrease in temperature [ 35 ].

https://doi.org/10.1371/journal.pone.0273384.t001

The major land use transformation was observed from shrubland to forest cover (2786.04 km 2 ), followed by agriculture to forest cover (2761.02 km 2 ), bare land to grassland (2116.62 km 2 ), agricultural land to grassland (1580.76 km 2 ), grassland to bare land (1575.9 km 2 ), agricultural land to settlement (1123.38 km 2 ), forest cover to agricultural land (975.06 km 2 ), shrubland to agricultural land (859.5 km 2 ), grassland to agriculture (824.67 km 2 ) and grassland to forest cover 400.41 km 2 ( Fig 2C , Table 1A ). The major increase of built-up land evidently found in major urban centres including Dhaka, Tezpur, Guwahati, Lakhimpur, Dibrugarh, Tinsukia, Shillong, and Itanagar. while the wetland primarily increased in the lower part of Arunachal Pradesh and Assam region altering agricultural land (0.36 km 2 ), shrubland (0.09 km 2 ), forest (33.3 km 2 ), and water body (2.61 km 2 ). The increase of forest cover was evident in southern Bangladesh including Dhaka, Barisal, Khulna, Chittagong, parts of Assam (Karbi Anglong, North Cachar Hills, Kamrup-Guwahati, Bongaigaon, and eastern part of Dhubri), Meghalaya (southern part of West Garo Hills, East Garo Hills, South Garo Hills, West Khasi Hills and East Khasi Hills, and small portion in the Jaintia Hills), Nagaland (Wokha, Mokokchung, Mon, Zunheboto, Dimapur, and Kohima), Arunachal Pradesh (Tirap, Changlang, Lohit, Lower Dibang Valley, East-West Siang, Subansiri and small area of Kameng) and southern hills of Bhutan.

ESA-CCI Land use/land cover map of (a) 2000 (b) 2019 (c) Land Use Transformations (LUT) with change code. Details of the change code discussed in S1 Table .

https://doi.org/10.1371/journal.pone.0273384.g002

4.2 Analysing precipitation and river discharge in Brahmaputra basin

The GPM final run-based daily precipitation data were used to deduce cumulative monthly variation of precipitation pattern in the Brahmaputra Basin 2015–2020 (May to August). The study exhibited a spatial variability in cumulative monthly precipitation pattern with extremely high precipitation (>2000 mm) in the lower parts of the basin in contrast to low precipitation (~50–200 mm) in upperparts. Very high precipitation (>900 mm) was observed in highly mountainous terrain area in the lower Brahmaputra Basin (mainly Meghalaya) during June 2015, while the high precipitation (600 to >900 mm) was observed in the major parts of the lower Brahmaputra Basin during June to August 2015, and low precipitation (200 mm– 600 mm) in the part middle part of Brahmaputra Basin and SW Bangladesh and very low precipitation (<200 mm) in the upper Brahmaputra Basin during May to August 2015 ( Fig 3 ). In 2016, there very high precipitation was observed in the Assam valley and some NE Bangladesh during July while a high precipitation in a major part of NE India (Assam valley) and south Bangladesh, alternatively the low (300 – 600mm) precipitation in the entire lower and central part of Brahmaputra Basin ( Fig 3 ). In 2017, high precipitation was observed mainly in the Assam valley and upper parts of Bangladesh during June and August. While moderate precipitation (400 - >800 mm) was observed in parts of the lower Brahmaputra Basin from May to August, in contrast to low precipitation (<400 mm) in the upper part Brahmaputra Basin during the monsoonal period. In 2018, a moderately high (400–800 mm) precipitation was observed in the part of lower Brahmaputra Basin except for lower part of Bangladesh region ( Fig 3 ). In 2019, the high precipitation (>900 mm) was observed in the Assam and Arunachal Pradesh valley) and north-eastern part of Bangladesh during June—July, and a low precipitation in the upper part of Brahmaputra Basin and the south-west Bangladesh ( Fig 3 ). In 2020, very high precipitation was observed in lower Assam and Arunachal Pradesh and northern Bangladesh during June and July with high intensity of precipitation, a moderately high (300–700 mm) precipitation was observed in the lower part of Brahmaputra Basin barring southern Bangladesh, alternatively a low precipitation in the central part of Brahmaputra Basin and southern Bangladesh ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0273384.g003

The precipitation anomaly exhibited a high anomalous precipitation pattern (>2500 mm) in a major part of NE India and NE Bangladesh while a moderate anomalous precipitation pattern (900–2500 mm) in the lower Brahmaputra Basin and the low (100–900 mm) precipitation was observed at the upper part of Brahmaputra Basin from 2015 to 2020 ( Fig 4 ). Highly intensive precipitation was observed in the lower parts of Brahmaputra Basin including Meghalaya, Assam, Arunachal Pradesh, Sylhet, and Dhaka. While the standard anomaly of precipitation during 2015 was observed as the positive anomalous precipitation in the entire lower Brahmaputra Basin with eastern part of Tibet region. In 2016, anomalous precipitation was observed in southern part of Bangladesh while the negative low anomalous precipitation observed in the entire Brahmaputra Basin. In 2017, the primary high anomalous precipitation was observed in the entire Brahmaputra Basin barring the eastern parts of the lower Basin (negative part). In 2018 and 2019, the negative anomalous precipitation (exhibiting reduction in precipitation) was observed in major parts of Brahmaputra Basin barring the upper east and the Assam valley, which recorded positive anomalous precipitation. In 2020, northern Bangladesh, the southern part of NE India, and the eastern part of Tibet had positive precipitation anomalies barring a few regions.

https://doi.org/10.1371/journal.pone.0273384.g004

The high river discharge during May to September months are also one of the reasons for flood inundation in the Brahmaputra basin ( Fig 5 ). The high discharge (16,000 to– 18,000 m 3 s -1 ) during August 2017 and July 2020 is analogous to the high precipitation in the region ( Fig 6 ). The study highlights that the river discharge depends on the precipitation concentrations as observed during precipitation events primarily in upper catchment and accumulation in lower catchment ultimately leading to floods in the lower Brahmaputra basin. The positive normalized anomalous precipitation comprised a large area in the months of May, June, and August, but later in July, the negative normalized anomalous precipitation was recorded. High increasing levels mainly showed in the central parts in Brahmaputra Basin, alternatively, in July the middle part highly decreased and the upper part steadily. In 2017, high variations of precipitation were observed in monsoonal periods except for August because it was observed in highly positive normalized precipitation. While in 2019, the positive normalized precipitation was observed in May and July months at the core flood-prone zone of the study area.

https://doi.org/10.1371/journal.pone.0273384.g005

https://doi.org/10.1371/journal.pone.0273384.g006

4.3 Impact of flood inundation in Brahmaputra basin

The Brahmaputra River has a long history of flood leading to large destruction almost every year and therefore, the study highlights the use of SAR multi-temporal datasets with precipitation and river discharge to monitor the flood inundation. During late summer and monsoon melting of glaciers and high precipitation especially in the north-eastern part of India accumulate more water in river and leads to very high river discharge, leading to flood inundation in the lower part of basin. The cumulative impact of flood inundation was significantly observed on agricultural land with highest inundation during 2017 (36952.45 km 2 ; 90.96% of total LULC in the basin) and minimum during 2016 (34790.22 km 2 ; 77 .84%). Forest is the second most affected flood with a highest inundation during 2016 (5.40%; 2413.12 km 2 ) and a lowest inundation during 2017 (2.74%; 1111.72 km 2 ) disturbed the biodiversity, farming, and also wildlife ( Fig 7 ). In contrast, the settlement areas were slightly low in coverage but had large impacts on mankind and were primarily affected during 2016 (1758.19 km 2 ), and least during 2018 (426.42 km 2 ) ( Table 2 ; Fig 7 ). Although the large parts of the Brahmaputra basin are under cultivation and have been the largest impacts due to flood inundation. Bangladesh is mainly covered by rivers and the majority of its parts are affected by the flood inundation during monsoon and post-monsoon periods. In 2020, Bangladesh’s highly affected area is 41266 km 2 and the lowest affected area is in 2015, 25690.49 km 2 . And then in India Assam valley was invaded by floods generally each year 2018 and 2019 were the most impactful times for Assam (10173.78 km 2 and 10172.78 km 2 ), but in 2020 it was the least affected 8920.18 km 2 . West Bengal was also oppressed by floods in most of the year, in 2016 there was a high impact of (2828.53 km 2 ) and then it slowly lessened towards 2020. In contrast, flood inundation has a lower impact on the area of China, Bhutan, and some parts of NE India (Meghalaya, Arunachal Pradesh) countries due to its safer site and situation ( Table 1B ).

Impact of flood inundation on ESA-CCI LULC for Brahmaputra Basin during (a) 2015 (b) 2016 (c) 2017 (d) 2018 (e) 2019 (f) 2020. Contains information from OpenStreetMap and OpenStreetMap Foundation, which is made available under the Open Database License.

https://doi.org/10.1371/journal.pone.0273384.g007

https://doi.org/10.1371/journal.pone.0273384.t002

5. Conclusion

Flood is one of the most common phenomena in the Brahmaputra Basin affecting a large area, assets, and population during monsoon and post-monsoon periods. The present study highlights the spatio-temporal patterns of flood inundation in the entire Brahmaputra Basin analysing its main causative factors viz ., cumulative precipitation patterns (2015 to 2020). The study exhibited a high (900–2000 mm) to very high (>2000 mm) monthly cumulative precipitation in the part of NE India (Mawsynram of Khasi hill in Meghalaya) and NE Bangladesh in the Brahmaputra Basin. The normalized precipitation indicated a rise in precipitation patterns in the Central and lower parts of the Brahmaputra Basin during 2015–2020. The temporal optical satellite data based LULC change exhibited a significant decline in shrubland (28.66%, 3218.67 km 2 ), in contrast to a significant rise in settlement (290.16%; 1282.23 km 2 ) in the region. The high to the moderate impact of flood inundation observed in Bangladesh (vary between 63% to 78% compared to total inundation in the basin); Assam (vary between 14% to 25% compared to total inundation in the basin), and West Bengal (vary between 4% to 6.5% compared to total inundation in the basin). The flood had severely affected ~85% of total agricultural land, and ~2% of settlements in the Brahmaputra Basin during the observation periods. Although the lower Brahmaputra Basin has a long history of flood inundation, the recent built-up growth and significant land use transformation exacerbate flood vulnerability and risk to a greater extent. Thus, there is an urgent need to reduce the increasing impact of floods by adopting proper afforestation measures in the upper catchments to reduce the soil erosion; the de-siltation process of the Brahmaputra River in Assam and Bangladesh to increase the depth of the river. Nevertheless, the region is prone to flood, and thus the controlled built-up development is pertinent. Settlements in the study region are surrounded by enumerable operational holdings of varying shapes and sizes. This risk shows high population pressure on arable land that leads to further fragmentation. The study will help to contribute towards sustainable land use planning and management towards the protection of extremely rich biodiversity of the Brahmaputra valley.

Supporting information

S1 table. area (in km 2 ) transformation and code for lut of lulc corresponding to year 2001 from 2019 as shown in fig 2 ..

https://doi.org/10.1371/journal.pone.0273384.s001

Acknowledgments

Preet Lal is thankful to Michigan State University library for open access funding through open access journal publishing agreements with PLOS One. All authors are thankful to editor and anonymous reviewers for critical comments on manuscripts. All authors are also thankful to the ESA Copernicus hub, NASA, and the European Commission for providing free data archives used in the study.

• View Article
• Google Scholar
• 9. Bronstert A, Crisologo I, Heistermann M, Ozturk U, Vogel K, Wendi D. Flash-Floods: More Often, More Severe, More Damaging? An Analysis of Hydro-geo-environmental Conditions and Anthropogenic Impacts. In: Leal Filho W, Nagy GJ, Borga M, Chávez Muñoz PD, Magnuszewski A, editors. Climate Change, Hazards and Adaptation Options. Cham: Springer International Publishing; 2020. pp. 225–244. https://doi.org/10.1007/978-3-030-37425-9_12
• PubMed/NCBI
• 28. Kumar R. Flood Damage Assessment in a Part of the Ganga-Brahmaputra Plain Region, India. Advances in Remote Sensing for Natural Resource Monitoring. John Wiley & Sons, Ltd; 2021. pp. 389–404. https://doi.org/10.1002/9781119616016.ch19

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 05 January 2015

Flood risk of natural and embanked landscapes on the Ganges–Brahmaputra tidal delta plain

• L. W. Auerbach 1 ,
• S. L. Goodbred Jr 1 ,
• D. R. Mondal 2 ,
• C. A. Wilson 1 ,
• K. R. Ahmed 3 ,
• M. S. Steckler 4 ,
• C. Small 4 ,
• J. M. Gilligan 1 &
• B. A. Ackerly 5  

Nature Climate Change volume  5 ,  pages 153–157 ( 2015 ) Cite this article

4756 Accesses

233 Citations

56 Altmetric

Metrics details

• Climate-change mitigation
• Environmental sciences
• Geomorphology

The Ganges–Brahmaputra river delta, with 170 million people and a vast, low-lying coastal plain, is perceived to be at great risk of increased flooding and submergence from sea-level rise 1 , 2 , 3 , 4 , 5 . However, human alteration of the landscape can create similar risks to sea-level rise. Here, we report that islands in southwest Bangladesh, enclosed by embankments in the 1960s, have lost 1.0–1.5 m of elevation, whereas the neighbouring Sundarban mangrove forest has remained comparatively stable 6 , 7 , 8 . We attribute this elevation loss to interruption of sedimentation inside the embankments, combined with accelerated compaction, removal of forest biomass, and a regionally increased tidal range. One major consequence of this elevation loss occurred in 2009 when the embankments of several large islands failed during Cyclone Aila, leaving large areas of land tidally inundated for up to two years until embankments were repaired. Despite sustained human suffering during this time 9 , 10 , the newly reconnected landscape received tens of centimetres of tidally deposited sediment, equivalent to decades’ worth of normal sedimentation. Although many areas still lie well below mean high water and remain at risk of severe flooding, we conclude that elevation recovery may be possible through controlled embankment breaches.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 print issues and online access

195,33 € per year

only 16,28 € per issue

Buy this article

• Purchase on Springer Link
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Degradation of floodplain integrity within the contiguous United States

Ryan R. Morrison, Kira Simonson, … Dan Carver

Quantitative assessment of the erosion and deposition effects of landslide-dam outburst flood, Eastern Himalaya

Xiaolu Dong, Xianyan Wang, … Huayu Lu

Mangrove removal exacerbates estuarine infilling through landscape-scale bio-morphodynamic feedbacks

Danghan Xie, Christian Schwarz, … Barend van Maanen

Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nature Geosci. 2 , 681–686 (2009).

Article   CAS   Google Scholar  

IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

Google Scholar  

Bhuiyan, M. J. A. N. & Dutta, D. Analysis of flood vulnerability and assessment of the impacts in coastal zones of Bangladesh due to potential sea-level rise. Nature Hazards 61 , 729–743 (2012).

Article   Google Scholar  

Dasgupta, S., Huq, M., Sohel Masud, M., Mukherjee, N. & Pandey, K. Climate proofing infrastructure in Bangladesh: The incremental cost of limiting future flood damage. J. Environ. Dev. 20 , 167–190 (2011).

Brammer, H. Bangladesh’s dynamic coastal regions and sea-level rise. Clim. Risk Manage. 1 , 51–62 (2014).

Allison, M. A. Historical changes in the Ganges–Brahmaputra delta front. J. Coast. Res. 14 , 1269–1275 (1998).

Goodbred, S. L. & Kuehl, S. A. Floodplain processes in the Bengal basin and the storage of Ganges–Brahmaputra river sediment: An accretion study using 137 Cs and 210 Pb geochronology. Sedim. Geol. 121 , 239–258 (1998).

Rogers, K. G., Goodbred, S. L. & Mondal, D. R. Monsoon sedimentation on the ‘abandoned’ tide-influenced Ganges–Brahmaputra Delta plain. Estuar. Coast. Shelf Sci. 131 , 297–309 (2013).

Cyclone Aila: Joint UN Multi-Sector Assessment & Response Framework 44 (United Nations, 2010)

Mehedi, H., Nag, A. K. & Farhana, S. Climate Induced Displacement—Case Study of Cyclone Aila in the Southwest Coastal Region of Bangladesh 37 (HumanityWatch, 2010).

Ericson, J., Vörösmarty, C., Dingman, S., Ward, L. B. & Meybeck, M. Effective sea-level rise and deltas: Causes of change and human dimension implications. Glob. Planet. Change 50 , 63–82 (2006).

Day, J. W., Pont, D., Hensel, P. F. & Ibañez, C. Impacts of sea-level rise on deltas in the Gulf of Mexico and the Mediterranean: The importance of pulsing events to sustainability. Estuaries 18 , 636–647 (1995).

Overeem, I. & Syvitski, J. P. M. Dynamics and Vulnerability of Delta Systems 54 LOICZ Reports Studies No. 35 (GKSS Research Center, LOICZ International Project Office, 2009)

Pethick, J. & Orford, J. D. Rapid rise in effective sea-level in southwest Bangladesh: Its causes and contemporary rates. Glob. Planet. Change 111 , 237–245 (2013).

Goodbred, S. L. & Kuehl, S. A. Holocene and modern sediment budgets for the Ganges–Brahmaputra river system: Evidence for highstand dispersal to flood-plain, shelf, and deep-sea depocenters. Geology 27 , 559–562 (1999).

Hanebuth, T. J. J., Kudrass, H. R., Linstadter, J., Islam, B. & Zander, A. M. Rapid coastal subsidence in the central Ganges–Brahmaputra Delta (Bangladesh) since the 17th century deduced from submerged salt-producing kilns. Geology 1–4 (2013).

Allison, M., Khan, S., Goodbred, S. & Kuehl, S. Stratigraphic evolution of the late Holocene Ganges–Brahmaputra lower delta plain. Sedim. Geol. 155 , 317–342 (2003).

Allison, M. & Kepple, E. Modern sediment supply to the lower delta plain of the Ganges–Brahmaputra River in Bangladesh. Geo-Marine Lett. 21 , 66–74 (2001).

Wilson, C. A. & Goodbred, S. L. Construction and maintenance of the Ganges–Brahmaputra–Meghna delta: Linking process, morphology, and stratigraphy. Annu. Rev. Mar. Sci. 7 http://dx.doi.org/10.1146/annurev-marine-010213-135032 (2015)

Giri, C., Pengra, B., Zhu, Z., Singh, A. & Tieszen, L. L. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar. Coast. Shelf Sci. 73 , 91–100 (2007).

Stanley, D. J. & Warne, A. G. Nile Delta in its destruction phase. J. Coast. Res. 14 , 794–825 (2012).

Day, J. W. et al. Restoration of the Mississippi Delta: Lessons from Hurricanes Katrina and Rita. Science 315 , 1679–1684 (2007).

Rahman, R. & Salehin, M. in. in Disaster Risk Reduction. Approaches Bangladesh (eds Shaw, R., Mallick, F. & Islam, A.) 65–90 (Springer, 2013).

Chapter   Google Scholar  

Ellison, A. M., Mukherjee, B. B. & Karim, A. Testing patterns of zonation in mangroves: Scale dependence and environmental correlates in the Sundarbans of Bangladesh. J. Ecol. 88 , 813–824 (2000).

Goodbred, S. L. & Kuehl, S. A. The significance of large sediment supply, active tectonism, and eustasy on margin sequence development: Late Quaternary stratigraphy and evolution of the Ganges–Brahmaputra delta. Sedim. Geol. 133 , 227–248 (2000).

Komiyama, A., Poungparn, S. & Kato, S. Common allometric equations for estimating the tree weight of mangroves. J. Trop. Ecol. 21 , 471–477 (2005).

Stanley, D. J. & Hait, A. K. Holocene depositional patterns, neotectonics, and Sundarban mangroves in the western Ganges–Brahmaputra delta. J. Coast. Res. 16 , 26–39 (2000).

Download references

Acknowledgements

We thank D. Datta, B. Kumar, S. Hossain, Md R. Karim and F. Akter for their assistance with field data collection. As noted Figs  1 , 3c , and Supplementary Fig. 8 include copyrighted material of DigitalGlobe, Inc., All Rights Reserved. Figure 3a, b includes Landsat imagery distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at USGS/EROS, Sioux Falls, South Dakota ( http://lpdaac.usgs.gov ). This research was financially supported by grants from the Office of Naval Research (N00014-11-1-0683) and National Science Foundation–Belmont Forum (No. 1342946). This paper is a contribution to the ICSU 2015 Sustainable Deltas Initiative.

Author information

Authors and affiliations.

Department of Earth and Environmental Sciences, Vanderbilt University, Nashville, Tennessee 37240, USA

L. W. Auerbach, S. L. Goodbred Jr, C. A. Wilson & J. M. Gilligan

School of Earth and Environmental Sciences, Queens College—City University of New York, Queens, New York 11367, USA

D. R. Mondal

Environmental Science Discipline, Khulna University, Khulna 9208 Bangladesh,

K. R. Ahmed & K. Roy

Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York 10964, USA

M. S. Steckler & C. Small

Department of Political Science, Vanderbilt University, Nashville, Tennessee 37203, USA

B. A. Ackerly

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed extensively to the work presented in this paper. L.W.A., S.L.G.Jr, M.S.S. and D.R.M. designed the field project. L.W.A., S.L.G.Jr, D.R.M., C.A.W., K.R.A. and K.R. collected data. L.W.A., S.L.G.Jr, D.R.M., C.A.W., M.S.S. and C.S. processed and analysed data. L.W.A., S.L.G.Jr, C.A.W. and J.M.G. wrote the manuscript. L.W.A., S.L.G.Jr, D.R.M., M.S.S. and C.A.W. wrote the Supplementary Information . All authors discussed the results and implications and commented on the manuscript at all stages.

Corresponding author

Correspondence to S. L. Goodbred Jr .

Ethics declarations

Competing interests.

The authors declare no competing financial interests.

Supplementary information

Supplementary information (pdf 13531 kb), rights and permissions.

Reprints and permissions

About this article

Cite this article.

Auerbach, L., Goodbred Jr, S., Mondal, D. et al. Flood risk of natural and embanked landscapes on the Ganges–Brahmaputra tidal delta plain. Nature Clim Change 5 , 153–157 (2015). https://doi.org/10.1038/nclimate2472

Download citation

Received : 11 March 2014

Accepted : 20 November 2014

Published : 05 January 2015

Issue Date : February 2015

DOI : https://doi.org/10.1038/nclimate2472

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Aligning the global delta risk index with sdg and sfdrr global frameworks to assess risk to socio-ecological systems in river deltas.

• Emilie Cremin
• Jack O’Connor
• Fabrice G. Renaud

Sustainability Science (2023)

Coastal Salinity and Water Management Practices in the Bengal Delta: A Critical Analysis to Inform Salinisation Risk Management Strategies in Asian Deltas

• Sean E. Feist
• Mohammad A. Hoque
• Kazi M. Ahmed

Earth Systems and Environment (2023)

Sediment delivery to sustain the Ganges-Brahmaputra delta under climate change and anthropogenic impacts

• Jessica L. Raff
• Steven L. Goodbred
• Lauren A. Williams

Nature Communications (2023)

The unequal distribution of water risks and adaptation benefits in coastal Bangladesh

• Emily J. Barbour
• Mohammed Sarfaraz Gani Adnan
• Jim W. Hall

Nature Sustainability (2022)

Sea level rise risks and societal adaptation benefits in low-lying coastal areas

• Alexandre K. Magnan
• Michael Oppenheimer
• Hans-Otto Pörtner

Scientific Reports (2022)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

News from the Columbia Climate School

Future Brahmaputra River Flooding as Climate Warms May Be Underestimated, Study Says

Kevin Krajick

A new study looking at seven centuries of water flow in south Asia’s mighty Brahmaputra River suggests that scientists are underestimating the river’s potential for catastrophic flooding as climate warms. The revelation comes from examinations of tree rings, which showed rainfall patterns going back centuries before instrumental and historical records.

Many researchers agree that warming climate will intensify the seasonal monsoon rains that drive the Brahmaputra, but the presumed baseline of previous natural variations in river flow rests mainly on discharge-gauge records dating only to the 1950s. The new study, based on the rings of ancient trees in and around the river’s watershed, shows that the post-1950s period was actually one of the driest since the 1300s. The rings show that there have been much wetter periods in the past, driven by natural oscillations that took place over decades or centuries. The takeaway: destructive floods probably will come more frequently than scientists have thought, even minus any effects of human-driven climate change. Estimates probably fall short by nearly 40 percent, say the researchers. The findings were just published in the journal Nature Communications.

“The tree rings suggest that the long-term baseline conditions are much wetter than we thought,” said Mukund Palat Rao, a recent PhD. graduate of Columbia University’s Lamont-Doherty Earth Observatory and lead author of the study. “Whether you consider climate models or natural variability, the message is the same. We should be prepared for a higher frequency of flooding than we are currently predicting.”

The Brahmaputra is one of the world’s mightiest rivers, flowing under a variety of names and braided routes some 2,900 miles through Tibet, northeast India and Bangladesh. Near its mouth, it combines with India’s Ganga River to create the world’s third largest ocean outflow, behind only the Amazon and the Congo. (It is tied with Venezuela’s Orinoco.) At points, it is nearly 12 miles wide. Its delta alone is home to 130 million Bangladeshis, and many millions more live upstream.

Interactive Map: Flooding in Bangladesh, July 2020

According to the UN, about 30 million Bangladeshis were exposed or living close to flooded areas during July 2020. Flood areas are shown in blue; districts are shaded red by population potentially exposed. Data: Unitar / Unosat and NOAA. (Areas have been simplified.) Use mousewheel, buttons, or double-click to zoom; drag and drop to pan. Hover over districts to view flood data. Use buttons at lower left to show/hide layers. View large version

The river routinely floods surrounding areas during the July-September monsoon season, when moisture-laden winds sweep in from the Indian Ocean and bring rain along its length, from its Himalayan headwaters on down to the coastal plain. As with the Nile, the flooding has a good side, because the waters drop nutrient-rich sediment to replenish farmland, and some degree of flooding is essential for rice cultivation. But some years, the flooding runs out of control, and low-lying Bangladesh gets hit hardest. In 1998, 70 percent of the country went underwater, taking out crops, roads and buildings, and killing many people. Other serious floods came in 2007 and 2010. In September 2020 the worst flooding since 1998 was still underway , with a third of Bangladesh inundated, and 3 million people rendered homeless.

Higher temperatures drive more evaporation of ocean waters, and in this region that water ends up as rainfall on land during the monsoon. As a result, most scientists think that warming climate will intensify the monsoon rains in coming decades, and in turn increase seasonal flooding. The question is, how much more often might big floods happen in the future?

The authors of the new study first looked at records from a river-flow gauge in northern Bangladesh. This showed a median discharge some 41,000 cubic meters per second from 1956 to 1986, and 43,000 from 1987 to 2004. (In the big flood year of 1998, peak discharge more than doubled.)

They then looked at data from the rings of ancient trees that researchers sampled at 28 sites in Tibet, Myanmar, Nepal and Bhutan, at sites within the Brahmaputra watershed, or close enough to be affected by the same weather systems. Most samples were taken from conifer species in the last 20 years by scientists from the Lamont-Doherty Tree Ring Lab, led by study coauthor Edward Cook. Since people have long been cutting down trees in populous areas, Cook and his colleagues sometimes hiked for weeks to reach undisturbed sites in remote, mountainous terrain. Straw-width samples were bored from trunks, without damage to the trees. The oldest tree they found, a Tibetan juniper, dated to the year 449.

Back at the lab, they analyzed the tree rings, which grow wider in years when soil moisture is high, and thus indirectly reflect rainfall and resulting river runoff. This allowed the scientists to assemble a 696-year chronology, running from 1309 to 2004. By comparing the rings with modern instrumental records as well as historical records going back to the 1780s, they could see that the widest rings lined up neatly with known major flood years. This in turn allowed them to extrapolate yearly river discharge in the centuries preceding modern records. They found that 1956-1986 was in only the 13th percentile for river discharge, and 1987-2004 in the 22nd.

This, they say, means that anyone using the modern discharge record to estimate future flood hazard would be underestimating the danger by 24 to 38 percent, based solely on natural variations; human driven warming would have to be added on top of that. “If the instruments say we should expect flooding toward the end of the century to come about every four and a half years, we are saying we should really expect flooding to come about every three years,” said Rao.

The tree rings do show some other relatively dry times, in the 1400s, 1600s and 1800s. But they also show very wet periods of extreme flooding with no analog in the relatively brief modern instrumental period. The worst lasted from about 1560-1600, 1750-1800 and 1830-1860.

Climate change will almost certainly affect the flow of other major rivers in the region, though not necessarily in the same ways. The mighty Ganga, flowing mainly through India, is also powered mainly by the monsoon, so it will likely behave much like the Brahmaputra. But the Indus, which flows through Tibet, India and Pakistan, derives most of its flow not from the monsoon, but rather from the winter buildup of snow and ice in Himalayan glaciers, and subsequent melting in summer. In 2018 Rao and colleagues published a tree-ring study showing that the river’s flow has been anomalously high in recent years. They suggest that as climate warms and the glaciers undergo accelerated melting, the Indus will supply plenty of needed irrigation water—but at some point, when the glaciers lose enough mass, the seasonal spigot will turn the other way, and there may not be enough water.

Human vulnerability to floods along the Brahmaputra has increased in recent years due not only to sheer water volume, but because population and infrastructure  are growing fast. On the other hand, accurate flood warnings have become more advanced, and this has helped many villages reduce economic and social losses. “High discharges will continue to be associated with an increased likelihood of flood hazard in the future,” write the study authors. But, they say, this could be counteracted to some extent by “potential changes in policy, land use, or infrastructure that may ameliorate flood risk.”

The study was also coauthored by Benjamin Cook, Rosanne D’Arrigo, Brendan Buckley and Daniel Bishop, all affiliated with the Lamont-Doherty Tree Ring Lab; Upmanu Lall of the Columbia Water Center; Columbia University ecologist Maria Uriarte; and collaborators at other U.S. universities and in Australia and China.

Related Posts

In New Jersey’s Ancient Rocks, Hunting for Clues to an Earthquake in 2024

Columbia Beautiful Planet 2024

Plugging the Leak on Laundry Pollution

Celebrate over 50 years of Earth Day with us all month long! Visit our Earth Day website for ideas, resources, and inspiration.

Get the Columbia Climate School Newsletter →

Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India

Affiliations.

• 1 Centre for Disaster Management and Mitigation, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
• 2 Centre for Disaster Management and Mitigation, Vellore Institute of Technology, Vellore, Tamil Nadu, India. [email protected].
• PMID: 31755058
• DOI: 10.1007/s11356-019-06849-6

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as - 18 to - 30 dB and the range is identified as - 19 to - 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km 2 on peak flood date for the chosen study area.

Keywords: Assam 2017 floods; Colour segmentation; Histogram analysis; Statistical distribution; Visual interpretation map.

• Environmental Monitoring / methods*
• Remote Sensing Technology*

Grants and funding

• NDM-01/Indian Space Research Organisation

Call us @ 08069405205

Search Here

• An Introduction to the CSE Exam
• Personality Test
• Annual Calendar by UPSC-2024
• Common Myths about the Exam
• About Insights IAS
• Our Mission, Vision & Values
• Director's Desk
• Meet Our Team
• Our Branches
• Careers at Insights IAS
• Daily Current Affairs+PIB Summary
• Insights into Editorials
• Insta Revision Modules for Prelims
• Current Affairs Quiz
• Static Quiz
• Current Affairs RTM
• Insta-DART(CSAT)
• Insta 75 Days Revision Tests for Prelims 2024
• Secure (Mains Answer writing)
• Secure Synopsis
• Ethics Case Studies
• Insta Ethics
• Weekly Essay Challenge
• Insta Revision Modules-Mains
• Insta 75 Days Revision Tests for Mains
• Secure (Archive)
• Anthropology
• Law Optional
• Kannada Literature
• Public Administration
• English Literature
• Medical Science
• Mathematics
• Commerce & Accountancy
• Monthly Magazine: CURRENT AFFAIRS 30
• Content for Mains Enrichment (CME)
• InstaMaps: Important Places in News
• Weekly CA Magazine
• The PRIME Magazine
• Insta Revision Modules-Prelims
• Insta-DART(CSAT) Quiz
• Insta 75 days Revision Tests for Prelims 2022
• Insights SECURE(Mains Answer Writing)
• Interview Transcripts
• Previous Years' Question Papers-Prelims
• Answer Keys for Prelims PYQs
• Solve Prelims PYQs
• Previous Years' Question Papers-Mains
• UPSC CSE Syllabus
• Toppers from Insights IAS
• Testimonials
• Felicitation
• UPSC Results
• Indian Heritage & Culture
• Ancient Indian History
• Medieval Indian History
• Modern Indian History
• World History
• World Geography
• Indian Geography
• Indian Society
• Social Justice
• International Relations
• Agriculture
• Environment & Ecology
• Disaster Management
• Science & Technology
• Security Issues
• Ethics, Integrity and Aptitude

• Indian Heritage & Culture
• Enivornment & Ecology

Rivers in News

Facts for prelims (ffp).

• Our Mission, Vision & Values
• Director’s Desk
• Commerce & Accountancy
• Previous Years’ Question Papers-Prelims
• Previous Years’ Question Papers-Mains
• Environment & Ecology
• Science & Technology

Interaction between the braided bar and adjacent channel during flood: a case study of a sand-bed braided river, Brahmaputra–Jamuna

• Original Article
• Published: 14 July 2018
• Volume 5 , pages 947–960, ( 2019 )

Cite this article

• Shampa   ORCID: orcid.org/0000-0001-9650-960X 1 &
• Mohammad Mostafa Ali 2  

452 Accesses

15 Citations

Explore all metrics

The braided river is characterized by numerous bars which exhibit chaotic nature and add uncertainty to understand and prediction of the river network. The development of these bars may be affected by several factors like boundary conditions, forcing and non-linear physical processes. Here, in this research, some prevailing physical processes which may affect the growth of bar after its emergence have been examined. Then, attempts have been made to identify the relationships between the bars and their adjacent channels. For this purpose, a two-dimensional-depth-averaged morphodynamic model, Delft3D was used to study the interaction between the braided bar and adjacent channel during a flood in a compound bar dominated area at a large sand-bed braided river-Brahmaputra–Jamuna. The results showed that the 2D model can reproduce the major physical processes which are responsible for the growth of the bar. The study showed that the size and shape of the bars were dependent on the characteristics of the adjacent channels. The occurrence of chute channel over the bartop during the flood depends on the depth of flow over the bar during the flood.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Simulation of Flow Profile Response to Alternate Bar Formation in Rivers

Prospects of Modeling and Morpho-dynamic Study for Brahmaputra River

Dynamics of channel bar morphology on multi-decadal timescales in a braided river within Himalayan foreland basin, India

Supriya Ghosh, Prasanta Mandal & Biswajit Bera

Ashmore P (1982) Laboratory modelling of gravel braided stream morphology. Earth Surf Process Landf 7(3):201–225

Article   Google Scholar  

Ashmore P (1991) How do gravel-bed rivers braid? Can J Earth Sci 28(3):326–341

Ashworth PJ, Best JL, Roden JE et al (2000) Morphological evolution and dynamics of a large, sand braid-bar, Jamuna River, Bangladesh. Sedimentology 47:533–555. https://doi.org/10.1046/j.1365-3091.2000.00305.x

Ashworth P, Smith GS, Best J, Bridge JS, Sn Lane, Lunt IA, Thomas RE (2011) Evolution and sedimentology of a channel fill in the sandy braided South Saskatchewan Riverand its comparison to the deposits of an adjacent compound bar. Sediment 58(7):1860–1883

Bertoldi W, Zanoni L, Miori S et al (2009a) Interaction between migrating bars and bifurcations in gravel bed rivers. Water Resour Res https://doi.org/10.1029/2008WR007086

Google Scholar  

Bertoldi W, Zanoni L, Tubino M (2009b) Planform dynamics of braided streams. Process Landf 34(4):547–557

Best J, Ashworth P, Bristow C, Roden J (2003) Three-dimensional sedimentary architecture of a large, mid-channel sand braid bar, Jamuna River. Bangladesh. J Sediment Res 73(4):516–530

Best J, Woodward J, Ashworth P, Smith G (2006) Bar-top hollows: a new element in the architecture of sandy braided rivers. Sediment Geol 190(1–4):241–255

Bluck BJ (1979) Structure of coarse grained braided stream alluvium. Trans R Soc Edinb 70:181–221

Bridge JS (1993) The interaction between channel geometry, water flow, sediment transport and deposition in braided rivers. Geol Soc Lond Spec Publ 75:13–71

Church M, Jones D (1982) Channel bars in gravel-bed rivers. In: Hey R, Bathurst J, Thorne C (eds) Gravel-bed rivers. Wiley, New York, pp 291–324

Deltares (2009) Delft3D-FLOW user manual: simulation of multi-dimensional hydrodynamic flows and transport phenomena, including sediments, Technical report

Egozi R, Ashmore P (2009) Experimental analysis of braided channel pattern response to increased discharge. J Geophys Res Earth Surf 114:1–15. https://doi.org/10.1029/2008JF001099

Exner FM (1925) On the interaction between water and sediment in rivers. Akad Wiss Wien Math Naturwiss Klasse 134:165–204

Federici B, Paola C (2003) Dynamics of channel bifurcations in noncohesive sediments. Water Resour Res. https://doi.org/10.1029/2002WR001434

Federici B, Seminara G (2003) On the convective nature of bar instability. J Fluid Mech 487:S0022112003004737. https://doi.org/10.1017/S0022112003004737

Fujita Y (1989) Bar and channel formation. In: Ikeda S, Park G (eds) River meanders. American Geophysical Union Water Resources, Washington DC, pp 417–462

Chapter   Google Scholar  

Fujita Y, Muramoto Y (1982) Experimental study on stream channel in Alluvial rivers. Bull Disaster Prev Res Inst 32:49–96

Gupta A (2007) Large rivers: geomorphology and management. Wiley, Hoboken

Ikeda S (1984) Prediction of alternate bar wavelength and height. J Hydraul Eng 110:371–386. https://doi.org/10.1061/(ASCE)0733-9429(1984)110:4(371)

Ikeda S, Parker G (1989) River meandering. Water Resour Monogr 12. Am Geophys Union 12:379–416

ISPAN (1995) The Dynamic Physical and Human Environment of the Riverine Charlands and Charland, Socioeconomic RRA reports for the Brahmaputra–Jamuna, Meghna, Ganges and Padma rivers, and the Charland

Kabir MR, Ahmed N (1996) Bed shear stress for sediment transportation in the River Jamuna. J Civ Eng Inst Eng Bangladesh 24:55–68

Kleinhans MG, van den Berg JH (2011) River channel and bar patterns explained and predicted by an empirical and a physics-based method. Earth Surf Process Landf 36:721–738. https://doi.org/10.1002/esp.2090

Leopold LB, Wolman MG (1957) River channel patterns: braided, meandering and straight. US Geol Surv Prof Pap 282B:39–85

Lesser GR, Roelvink JA, van Kester JATM, Stelling GS (2004) Development and validation of a three-dimensional morphological model. Coast Eng 51:883–915. https://doi.org/10.1016/j.coastaleng.2004.07.014

Mosley MP (1983) Response of braided rivers to changing discharge. J Hydrol (New Zealand) 22:18–67

Parker G (1976) On the cause and characteristic scales of meandering and braiding in rivers. J Fluid Mech 76:457. https://doi.org/10.1017/S0022112076000748

Repetto R (2000) Unit processes in braided rivers. University of Genova, Genova

Rust BR (1978) Depositional models for braided alluvium. In: Miall AD (ed) Fluvial sedimentology. Canadian Society of Petroleum Geologists Memoirs, pp 605–626

Schuurman F, Marra WA, Kleinhans MG (2013) Physics-based modeling of large braided sand-bed rivers: bar pattern formation, dynamics, and sensitivity. J Geophys Res Earth Surf 118:2509–2527. https://doi.org/10.1002/2013JF002896

Schuurman F, Kleinhans MG, Middelkoop H (2016) Network response to disturbances in large sand-bed braided rivers. Earth Surf Dyn 4:25–45. https://doi.org/10.5194/esurf-4-25-2016

Shampa, Ali MM (2014) Development of bars in a large sand bed braided rivers. In: Viollet P-L (eds) Small scale morphological evolution of coastal, estuarine and river systems. Société Hydrotechnique de France, Nantes, pp 3–7

Shampa, Ali MM (2015) Numerical modeling of bar dynamics of a sand-bed braided river. In: 5th International conference on water and flood management, pp 41–52

Smith ND (1977) Some comments on terminology for bars in shallow rivers. Fluv Sedimentol (Ed AD Miall) 5:85–88

Thorne C (1982) Processes and mechanisms of river bank erosion. In: Hey RD, Bathurst JC, Thorne CR (eds) Gravel-bed rivers. Wiley, Chichester, pp 227–271

Van Rijn LC (1984) Sediment transport: bed load transport. J Hydraul Eng 110:1431–1456. https://doi.org/10.1061/(ASCE)0733-9429(1987)113:9(1187)

Van Der Wegen M, Roelvink JA (2008) Long-term morphodynamic evolution of a tidal embayment using a two-dimensional, process-based model. J Geophys Res Ocean 113:1–23. https://doi.org/10.1029/2006JC003983

Welber M, Bertoldi W, Tubino M (2012) The response of braided planform configuration to flow variations, bed reworking and vegetation: the case of the Tagliamento River, Italy. Earth Surf Process Landf 37:572–582. https://doi.org/10.1002/esp.3196

Yalin MS (1992) River mechanics. Pergamon Press, Oxford

Download references

Acknowledgements

This is research is funded by the first author’s M.Sc. research by the Bangladesh University of Engineering and Technology (BUET). The authors express their thanks to Bangladesh Water Development Board (BWDB) and Centre for Environmental and Geographic Information Services (CEGIS) for their immense support in data collection.

Author information

Authors and affiliations.

Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

Mohammad Mostafa Ali

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Shampa .

Rights and permissions

Reprints and permissions

About this article

Shampa, Ali, M.M. Interaction between the braided bar and adjacent channel during flood: a case study of a sand-bed braided river, Brahmaputra–Jamuna. Sustain. Water Resour. Manag. 5 , 947–960 (2019). https://doi.org/10.1007/s40899-018-0269-x

Download citation

Received : 27 November 2017

Accepted : 09 July 2018

Published : 14 July 2018

Issue Date : 01 June 2019

DOI : https://doi.org/10.1007/s40899-018-0269-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Bar dynamics
• Numerical model
• Adjacent channels
• Brahmaputra–Jamuna
• Find a journal
• Publish with us
• Track your research